The Resource Statistical methods for handling incomplete data, Jae Kwang Kim, Jun Shao

Statistical methods for handling incomplete data, Jae Kwang Kim, Jun Shao

Label
Statistical methods for handling incomplete data
Title
Statistical methods for handling incomplete data
Statement of responsibility
Jae Kwang Kim, Jun Shao
Creator
Contributor
Subject
Language
eng
Summary
"With the advances in statistical computing, there has been a rapid development of techniques and applications in missing data analysis. This book aims to cover the most up-to-date statistical theories and computational methods for analyzing incomplete data through (1)vigorous treatment of statistical theories on likelihood-based inference with missing data, (2) comprehensive treatment of computational techniques and theories on imputation, and (3) most up-to-date treatment of methodologies involving propensity score weighting, nonignorable missing, longitudinal missing, survey sampling application, and statistical matching. The book is suitable for use as a textbook for a graduate course in statistics departments or as a reference book for those interested in this area. Some of the research ideas introduced in the book can be developed further for specific applications"--
Member of
Assigning source
Provided by publisher
Cataloging source
DLC
http://library.link/vocab/creatorDate
1968-
http://library.link/vocab/creatorName
Kim, Jae Kwang
Dewey number
519.5/4
Illustrations
illustrations
Index
index present
LC call number
QA276.8
LC item number
.K55 2014
Literary form
non fiction
Nature of contents
  • bibliography
  • statistics
http://library.link/vocab/relatedWorkOrContributorName
Shao, Jun
Series statement
A Chapman & Hall book
http://library.link/vocab/subjectName
  • Missing observations (Statistics)
  • Multiple imputation (Statistics)
Label
Statistical methods for handling incomplete data, Jae Kwang Kim, Jun Shao
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Contents
  • How to use this book
  • Imputation models under past-value-dependent nonmonotone missing
  • 7.3.3.
  • Nonparametric regression imputation
  • 7.3.4.
  • Dimension reduction
  • 7.3.5.
  • Simulation study
  • 7.3.6.
  • Wisconsin Diabetes Registry Study
  • 7.4.
  • 2.
  • Random-effect-dependent missing data
  • 7.4.1.
  • Three existing approaches
  • 7.4.2.
  • Summary statistics
  • 7.4.3.
  • Simulation study
  • 7.4.4.
  • Modification of diet in renal disease
  • 8.
  • Likelihood-based approach
  • Application to survey sampling
  • 8.1.
  • Introduction
  • 8.2.
  • Calibration estimation
  • 8.3.
  • Propensity score weighting method
  • 8.4.
  • Fractional imputation
  • 8.5.
  • 2.1.
  • Fractional hot deck imputation
  • 8.6.
  • Imputation for two-phase sampling
  • 8.7.
  • Synthetic imputation
  • 9.
  • Statistical matching
  • 9.1.
  • Introduction
  • 9.2.
  • Introduction
  • Instrumental variable approach
  • 9.3.
  • Measurement error models
  • 9.4.
  • Causal inference
  • 2.2.
  • Observed likelihood
  • 2.3.
  • Mean score approach
  • 2.4.
  • Machine generated contents note:
  • Observed information
  • 3.
  • Computation
  • 3.1.
  • Introduction
  • 3.2.
  • Factoring likelihood approach
  • 3.3.
  • EM algorithm
  • 3.4.
  • 1.
  • Monte Carlo computation
  • 3.5.
  • Monte Carlo EM
  • 3.6.
  • Data augmentation
  • 4.
  • Imputation
  • 4.1.
  • Introduction
  • 4.2.
  • Introduction
  • Basic theory for imputation
  • 4.3.
  • Variance estimation after imputation
  • 4.4.
  • Replication variance estimation
  • 4.5.
  • Multiple imputation
  • 4.6.
  • Fractional imputation
  • 5.
  • 1.1.
  • Propensity scoring approach
  • 5.1.
  • Introduction
  • 5.2.
  • Regression weighting method
  • 5.3.
  • Propensity score method
  • 5.4.
  • Optimal estimation
  • 5.5.
  • Introduction
  • Doubly robust method
  • 5.6.
  • Empirical likelihood method
  • 5.7.
  • Nonparametric method
  • 6.
  • Nonignorable missing data
  • 6.1.
  • Nonresponse instrument
  • 6.2.
  • 1.2.
  • Conditional likelihood approach
  • 6.3.
  • Generalized method of moments (GMM) approach
  • 6.4.
  • Pseudo likelihood approach
  • 6.5.
  • Exponential tilting (ET) model
  • 6.6.
  • Latent variable approach
  • 6.7.
  • Outline
  • Callbacks
  • 6.8.
  • Capture-recapture (CR) experiment
  • 7.
  • Longitudinal and clustered data
  • 7.1.
  • Ignorable missing data
  • 7.2.
  • Nonignorable monotone missing data
  • 7.2.1.
  • 1.3.
  • Parametric models
  • 7.2.2.
  • Nonparametric p(y/x)
  • 7.2.3.
  • Nonparametric propensity
  • 7.3.
  • Past-value-dependent missing data
  • 7.3.1.
  • Three different approaches
  • 7.3.2.
Dimensions
25 cm.
Extent
xi, 211 pages
Isbn
9781439849637
Isbn Type
(hardback : acid-free paper)
Lccn
2013010114
Media category
unmediated
Media MARC source
rdamedia
Other physical details
illustrations
System control number
  • (CaMWU)u2968279-01umb_inst
  • 2811064
  • (Sirsi) i9781439849637
  • (OCoLC)613423472
Label
Statistical methods for handling incomplete data, Jae Kwang Kim, Jun Shao
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Contents
  • How to use this book
  • Imputation models under past-value-dependent nonmonotone missing
  • 7.3.3.
  • Nonparametric regression imputation
  • 7.3.4.
  • Dimension reduction
  • 7.3.5.
  • Simulation study
  • 7.3.6.
  • Wisconsin Diabetes Registry Study
  • 7.4.
  • 2.
  • Random-effect-dependent missing data
  • 7.4.1.
  • Three existing approaches
  • 7.4.2.
  • Summary statistics
  • 7.4.3.
  • Simulation study
  • 7.4.4.
  • Modification of diet in renal disease
  • 8.
  • Likelihood-based approach
  • Application to survey sampling
  • 8.1.
  • Introduction
  • 8.2.
  • Calibration estimation
  • 8.3.
  • Propensity score weighting method
  • 8.4.
  • Fractional imputation
  • 8.5.
  • 2.1.
  • Fractional hot deck imputation
  • 8.6.
  • Imputation for two-phase sampling
  • 8.7.
  • Synthetic imputation
  • 9.
  • Statistical matching
  • 9.1.
  • Introduction
  • 9.2.
  • Introduction
  • Instrumental variable approach
  • 9.3.
  • Measurement error models
  • 9.4.
  • Causal inference
  • 2.2.
  • Observed likelihood
  • 2.3.
  • Mean score approach
  • 2.4.
  • Machine generated contents note:
  • Observed information
  • 3.
  • Computation
  • 3.1.
  • Introduction
  • 3.2.
  • Factoring likelihood approach
  • 3.3.
  • EM algorithm
  • 3.4.
  • 1.
  • Monte Carlo computation
  • 3.5.
  • Monte Carlo EM
  • 3.6.
  • Data augmentation
  • 4.
  • Imputation
  • 4.1.
  • Introduction
  • 4.2.
  • Introduction
  • Basic theory for imputation
  • 4.3.
  • Variance estimation after imputation
  • 4.4.
  • Replication variance estimation
  • 4.5.
  • Multiple imputation
  • 4.6.
  • Fractional imputation
  • 5.
  • 1.1.
  • Propensity scoring approach
  • 5.1.
  • Introduction
  • 5.2.
  • Regression weighting method
  • 5.3.
  • Propensity score method
  • 5.4.
  • Optimal estimation
  • 5.5.
  • Introduction
  • Doubly robust method
  • 5.6.
  • Empirical likelihood method
  • 5.7.
  • Nonparametric method
  • 6.
  • Nonignorable missing data
  • 6.1.
  • Nonresponse instrument
  • 6.2.
  • 1.2.
  • Conditional likelihood approach
  • 6.3.
  • Generalized method of moments (GMM) approach
  • 6.4.
  • Pseudo likelihood approach
  • 6.5.
  • Exponential tilting (ET) model
  • 6.6.
  • Latent variable approach
  • 6.7.
  • Outline
  • Callbacks
  • 6.8.
  • Capture-recapture (CR) experiment
  • 7.
  • Longitudinal and clustered data
  • 7.1.
  • Ignorable missing data
  • 7.2.
  • Nonignorable monotone missing data
  • 7.2.1.
  • 1.3.
  • Parametric models
  • 7.2.2.
  • Nonparametric p(y/x)
  • 7.2.3.
  • Nonparametric propensity
  • 7.3.
  • Past-value-dependent missing data
  • 7.3.1.
  • Three different approaches
  • 7.3.2.
Dimensions
25 cm.
Extent
xi, 211 pages
Isbn
9781439849637
Isbn Type
(hardback : acid-free paper)
Lccn
2013010114
Media category
unmediated
Media MARC source
rdamedia
Other physical details
illustrations
System control number
  • (CaMWU)u2968279-01umb_inst
  • 2811064
  • (Sirsi) i9781439849637
  • (OCoLC)613423472

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